University of Iowa AI Action Plan
Following the “Guiding Principles for Artificial Intelligence at the University of Iowa,” the AI Steering Committee recommends the following actions. These actions, like the guiding principles, accept that what we teach, how we teach, what we research, how we research, and how we work at the university will be impacted by AI. The university community must respond with the recognition that AI will amplify human potential in the pursuit of excellence and enhance collaboration and engagement, but these pursuits must be done responsibly and with integrity.
What we teach
AI will impact the careers of all students who graduate from the University of Iowa, regardless of major and chosen career path. To prepare students to succeed in this environment, we recommend:
Basic Knowledge of AI
Objective: Ensure all graduating students have a foundational understanding of AI.
Action 1: Collaborate with and across colleges and programs to integrate AI concepts into the general curriculum, including elements of the general education program, such as rhetoric, for all undergraduate, graduate, and professional students.
Action 2: Increase student awareness of AI support and resources through the student mobile app and other methods.
Outcome: Students will understand how AI can support their work, as well as potential limitations, biases, and inaccuracies associated with AI usage, and when and how to use AI effectively and responsibly.
Metric: Number of students taking general curriculum courses with AI elements
Metric: Number of students taking major, graduate, or professional courses with AI elements
Communicating with AI
Objective: Equip students with knowledge and skills for effective use of Large Language Models (LLMs) like ChatGPT for communication.
Action 3: Offer workshops, courses, and certificates focused on accessing and using AI tools for information retrieval and communication.
Outcome: Students will be proficient in using AI tools to access information and amplify their human skills and communicators.
Metric: Number of students who have participated in workshops, courses, and certificates focused on accessing and using AI tools for information retrieval and communication
Disciplinary knowledge
Objective: Ensure students understand AI's impact on and applications in their fields of study.
Action 4: Collaborate with colleges, departments, and faculty to incorporate AI-related topics and assignments into discipline-specific curricula.
Action 5: Coordinate course offerings, particularly at the PhD level, to ensure that the university is training the next generation of scholars on AI concepts and methods.
Outcome: Students will be able to apply AI knowledge and tools relevant to their areas of expertise.
Metric: Number of majors, graduate programs, and professional programs that offer content that explores how to apply AI knowledge and tools relevant to their areas of expertise.
Empowering student learning through AI
Objective: Introduce students to AI tools and practices that augment their learning, promote student success, and improve their experiences at the UI.
Action 6: Provide training and equitable access for AI technologies that support personalized and independent learning.
Outcome: Students will leverage AI to augment their educational experience and improve holistic student success.
Metric: Number of AI implementations designed to augment student learning
Objective: Make student course materials and learning experiences available to all students.
Action 7: Develop tools to enhance accessibility of digital content and systems for all students.
Outcome: All students will be able to engage in learning activities.
Stakeholder engagement
Objective: Engage stakeholders to align AI education with workforce needs and employer expectations.
Action 8: Conduct regular meetings with stakeholders, including business and industry representatives and alumni, to gather insights on AI trends and skills requirements for graduates.
Outcome: The curriculum will be continuously updated to meet the evolving demands of the workforce and foster the success of our graduates.
Metric: Satisfaction of rating of annual survey of employers on how well UI graduates are prepared to use AI in the workplace
How we teach
AI can improve pedagogy, offering students personalized instruction that reinforces what they are learning in the classroom.
Faculty training
Objective: Help faculty leverage AI for instruction to improve student success and faculty efficiency.
Action 9: Encourage colleges and departments to have faculty engage in general and discipline-specific training and support of AI technologies.
Action 10: Build and share an understanding of when AI effectively supports pedagogy and learning outcomes and when it does not.
Action 11: Build communities for faculty to engage with each other and support staff to explore, share best practices, and implement AI in their courses.
Action 12: Develop and implement AI-driven programs that help instructors develop course content and methods that are evidence-based to support teaching excellence.
Action 13: Deploy AI systems and analytics to analyze student performance data and provide insights to instructors.
Outcome: Faculty will be equipped to effectively integrate AI into their teaching practices.
Outcome: Faculty will assist students in understanding how to use AI in innovative, effective, and responsible ways.
Outcome: Our commitment to AI integration in pedagogical approaches will help students be more successful on campus and in their future endeavors.
Metric: Number of faculty participating in AI training
Metric: Number of faculty responding in the affirmative to having implemented AI tools or AI-based pedagogy
Experiment and Invest
Objective: Support experimentation with AI technologies that improve student learning.
Action 14: Pilot AI tools that enhance classroom pedagogy and scalable learning opportunities. Invest in successful technologies and expand their use.
Outcome: Increased adoption of appropriate AI tools for teaching and learning.
Metric: Number of pilot projects supported by the university or colleges
Objective: Amplify the human potential of academic advisors for more comprehensive student advising
Action 15: Offer AI-driven tools to academic advisors to reduce operational low value work and allow them to focus on high impact activities.
Outcome: Increased student success and retention
What we research
AI enables opportunities to create new knowledge through efficient, scalable, and cost-effective technologies. Research in AI and related technologies, and the application of findings from these pursuits, will have broad impacts across many other disciplines.
Inspiration
Objective: Inspire faculty to apply AI in innovative ways.
Action 16: Create opportunities for faculty to learn from each other and external experts on the opportunities to apply AI in their areas of interest.
Outcome: Faculty will accelerate research activities to stay competitive with peers.
Metric: Number of faculty participating in AI training
Metric: Number of faculty participating AI communities and AI lightning talks
Invest in support
Objective: Provide necessary support for implementing AI technologies in research.
Action 17: Allocate funding for tools, training and staffing to support faculty in AI adoption.
Outcome: AI will be available for our inspired faculty to pursue their research interests.
Metric: Number of staff with specific charge to support AI in faculty research
Metric: Amount of university and collegiate funding available to support AI applications in research
Objective: Assist researchers and university leadership in developing and realizing areas of research excellence.
Action 18: Implement AI tools to analyze research trends and identify emerging areas of excellence. Prioritize strategic growth areas based on AI insights.
Action 19: Use AI to identify and recommend funding opportunities based on researchers' interests and project needs.
Outcome: Increased research funding and impact of research activities.
How we research
AI has the potential to transform the way research and scholarly work is achieved in many if not all disciplines. It is critical that we enable our faculty to use this new technology in their work.
Increase AI literacy and share success stories
Objective: Create opportunities for faculty to learn from each other and external experts, inspiring new AI applications in research and creative works.
Action 20: Provide support to faculty to establish interdisciplinary AI interest groups and communities of practice, including seminars with guest lecturers featuring AI experts from within and outside the university.
Action 21: Develop an “AI Showcase” to share faculty successes and techniques for applying AI to their research.
Action 22: Encourage and support departments to invite discipline-specific AI guest speakers to help them understand what’s possible.
Action 23: Through short courses and consultation, help researchers understand how AI can identify patterns and insights in complex datasets that might be missed by human researchers.
Outcome: Faculty will be able to leverage AI tools in their discipline to advance their research and creative works.
Metric: Number of faculty attending AI training sessions
Metric: Number of faculty participating AI communities and AI lightning talk
Metric: Number of “AI Showcase” projects completed
Enable new collaborations
Objective: Foster collaboration across diverse disciplines by using AI to enable researchers from different fields to work on common data-driven problems, provide tools that can be applied to various domains, from healthcare to environmental science, and facilitate the sharing of insights and methodologies across departments.
Action 24: Coordinate hiring of faculty in foundational AI to provide campus with AI expertise.
Action 25: Embrace interdisciplinary pilot research project opportunities offered by the Iowa Initiative for Artificial Intelligence with the goal of preparing extramural research grant submissions that use data-driven AI to progress research programs.
Action 26: Promote partnerships, including internal and external (both public and private sectors).
Action 27: Use AI to facilitate the formation of interdisciplinary research teams by identifying potential collaborators.
Outcome: A thriving culture of creativity, innovation, and interdisciplinary collaboration that supports growth in leading-edge research, scholarship, and creative activities.
Outcome: AI provides powerful tools for processing and analyzing massive datasets, enabling researchers to tackle complex problems at unprecedented speeds and scales.
Outcome: Interdisciplinary research teams will be easier to develop and more opportunities will be realized.
Metric: Number of foundational AI faculty
Metric: Number of projects supported by the Iowa Initiative for Artificial Intelligence
Metric: Grant funding that includes applications of AI
Invest in support structures for AI in research, scholarship, and creative activities
Objective: Leverage AI for generating new knowledge and creative work through efficient, scalable, and cost-effective AI technologies.
Action 28: Provide AI tools that can augment researchers' capabilities by automating routine tasks, allowing scholars to focus on higher-level analysis.
Action 29: Provide guidance on current AI technologies and their applications in various research, scholarship, and creative fields.
Action 30: Identify key AI tools and platforms suitable for the university's research, scholarship, and creative activities.
Action 31: Organize workshops and training sessions for faculty to familiarize themselves with these AI tools.
Action 32: Develop a pipeline for creating a sustainable AI workforce that addresses needs within the state of Iowa and beyond.
Action 33: Continue to assess AI support needs of the research, scholarly, and creative communities, and explore central and local support structures to meet those needs.
Action 34: Use AI to streamline administrative processes related to research.
Action 35: Implement AI to optimize the use of research spaces by analyzing usage patterns, funding opportunities, and other data sets
Outcome: Increased awareness, understanding, and adoption of AI tools among faculty members.
Outcome: Integration of AI technologies into ongoing and new research, scholarship, and creative projects.
Outcome: Increased efficiency and competitiveness for research and scholarly work through the effective adoption of AI.
Metric: Number of faculty reporting adopting AI tools in their research
Metric: Number of students who gain real world experience through assignments to research activities.
Metric: Number of students retained with AI skills
Metric: Number of faculty requests for AI tools that are met
Objective: Increase support for research activities outside of traditional funding methods.
Action 36: Use AI to identify and promote entrepreneurship opportunities by analyzing market trends and connecting researchers with potential partners and investors.
Outcome: Increased funding for research activities as a result of new entrepreneurship activities.
How we work
AI offers opportunities to more efficiently and effectively do the work that supports the university.
Staff training
Objective: Equip staff with the knowledge and skills to use AI technologies effectively.
Action 37: Develop and implement training programs covering AI basics and advanced capabilities.
Action 38: Organize workshops and seminars for hands-on AI tool usage.
Action 39: Continue to update guidance and policies to help faculty, staff, and students understand the security, privacy, appropriate use, and ethical aspects of adopting AI.
Outcome: Staff are proficient in AI applications relevant to their roles.
Metric: Number of staff attending AI training
Experiment and invest
Objective: Identify opportunities and adopt AI technologies that enhance university operations.
Action 40: Develop a P3 proposal for funding to develop and promote pilot projects to test AI applications in various departments.
Action 41: Continue and promote AI lighting talks to share how AI is proving useful on campus.
Action 42: Monitor the landscape and make available tools that are most helpful to faculty, staff, and students.
Outcome: A better understanding of tools and support needed for broader deployments.
Outcome: A broader understanding of AI and its opportunities and limitations.
Outcome: Increased efficiency and effectiveness in administrative tasks.
Metric: Cost savings tied to AI implementations
Metric: Lead time for task completion
Metric: Attendance at AI lightning talks
Metric: Funding received through P3 process
Invest in support
Objective: Provide support for the implementation of AI technologies.
Actions 43: Establish dedicated AI support teams to assist staff through:
Training
Consulting and individual support
Technical implementation teams for campus-generated ideas
Outcome: Smooth integration of AI technologies into daily operations.
Outcome: Enhanced productivity and reduced technical difficulties.
Metric: Investment in support for AI technology implementation and support
Metric: Number of support requests for AI technologies
How we deliver health care
As with the rest of the university’s work, AI will transform patient care and the business of health care.
Staff training
Objective: Equip health care staff with AI knowledge and skills.
Action 44: Develop training modules tailored for health care professionals.
Action 45: Conduct regular AI training sessions and simulations.
Outcome: Improved efficiency and effectiveness in health care delivery.
Outcome: Health care staff are adept at using AI to improve patient care.
Metric: Number of staff who have attended AI-related training
Experiment and invest
Objective: Incorporate AI technologies that enhance patient care.
Action 46: Initiate pilot projects to test AI in clinical settings.
Outcome: Enhanced patient care through proven AI technologies.
Outcome: More efficient health care operations.
Metric: Cost savings tied to AI implementations
Metric: Lead time for task completion
Invest in support
Objective: Ensure health care staff have the necessary support to implement AI.
Action 47: Create specialized AI support teams for health care professionals.
Action 48: Maintain ongoing assistance and updates for AI tools in health care.
Outcomes: Improved patient outcomes and operational efficiency.
Metric: Investment in support for AI technology implementation and support
Metric: Number of support requests for AI technologies
Summary
This action plan aims to leverage new AI technologies, ensuring that the University of Iowa stays competitive in a challenging market. This initiative will need to be prioritized over other ongoing activities due to limited resources. In the long run, prioritizing and investing in this area will enhance all the university's strategic missions.